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Geometry and vision : first International Symposium, ISGV 2021, Auckland, New Zealand, January 28-29, 2021, Revised Selected Papers / / edited by Minh Nguyen, Wei Qi Yan, and Harvey Ho
| Geometry and vision : first International Symposium, ISGV 2021, Auckland, New Zealand, January 28-29, 2021, Revised Selected Papers / / edited by Minh Nguyen, Wei Qi Yan, and Harvey Ho |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (407 pages) : illustrations |
| Disciplina | 006.37 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico | Computer vision |
| ISBN | 3-030-72073-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- A New Noise Generating Method Based on Gaussian Sampling for Privacy Preservation -- 1 Introduction -- 2 Related Work -- 2.1 Gaussian Noise Generating -- 2.2 Whittle's Noise Estimator -- 2.3 The Method Based on Fourier Transform -- 2.4 Distributed SGD for Differential Privacy -- 3 Our Methods -- 3.1 Contribution -- 3.2 The Process of Our Methods -- 3.3 Noise Variant in Stochastic Gradient Descent -- 3.4 Gaussian Distribution for Subsampling -- 4 Evaluations -- 4.1 Our Experiments for Comparing Learning Rates -- 4.2 Experiments for Gradient Clipping and Noise Levels -- 5 Conclusions -- References -- Traffic-Sign Recognition Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Research Design for Training Faster R-CNN -- 3.3 Research Design for Training Faster YOLOv5 -- 4 Results -- 4.1 Experiment Results of Faster R-CNN -- 4.2 Experiment Results of YOLOv5 -- 5 Analysis -- 6 Conclusion and Future Work -- References -- Tree Leaves Detection Based on Deep Learning -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Contribution -- 2 Literature Review -- 3 Methodology -- 3.1 Working Principle and Structure Analysis of YOLO -- 3.2 Analysis of the Working Principle of Faster R-CNN -- 3.3 Environmental Deployment -- 3.4 Data Set Preparation -- 3.5 Evaluation Methods -- 4 Analysis and Discussions -- 4.1 Comparison of Object Detection Results -- 4.2 Comparative Analysis of the Two Proposed Models -- 4.3 Discussions -- 5 Conclusion and Future Work -- References -- Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture -- 1 Introduction -- 2 Related Study -- 3 Materials and Methods -- 3.1 Encoder-Decoder Framework -- 3.2 Network Architecture Details.
4 Simulations and Results Discussion -- 4.1 Dataset Preparation -- 4.2 Quality Metrics -- 4.3 Comparison with Other State-of-the-Art Methods -- 5 Discussion and Conclusion -- References -- Apple Ripeness Identification Using Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Our Approaches -- 4 Our Experiments -- 5 Conclusion -- References -- A Hand-Held Sensor System for Exploration and Thermal Mapping of Volcanic Fumarole Fields -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 3.1 Sensor System and Registration -- 3.2 Datasets -- 3.3 Thermal Sensing Quality -- 4 Methods -- 4.1 Localization -- 4.2 3D Reconstruction - Direct -- 4.3 3D Reconstruction - Indirect -- 5 Results -- 6 Discussion -- 7 Conclusion -- References -- Traffic Sign Recognition Using Guided Image Filtering -- 1 Introduction -- 2 Literature Review -- 3 Network Design -- 3.1 Guided Image Filtering -- 3.2 Improved Faster R-CNN -- 3.3 Improved YOLOv5 -- 4 Results -- 4.1 Improved Faster R-CNN -- 4.2 Improved YOLOv5 -- 4.3 Comparsions of YOLOv5 and Faster R-CNN -- 4.4 Guided Image Filtering -- 5 Conclusion -- References -- Towards a Generic Bicubic Hermite Mesh Template for Cow Udders -- 1 Introduction -- 2 Methods -- 2.1 Data Cloud of Cow Udders -- 2.2 Bicubic Hermite Mesh -- 2.3 Coherent Point Drifting -- 3 Results -- 3.1 Morphing the CH Mesh of the Udder -- 3.2 Geometric Modelling for the Teat -- 4 Discussion -- 5 Conclusion -- References -- Sign Language Recognition from Digital Videos Using Deep Learning Methods -- 1 Introduction -- 2 Related Work -- 3 Our Method -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- New Zealand Shellfish Detection, Recognition and Counting: A Deep Learning Approach on Mobile Devices -- 1 Introduction and Backgrounds -- 2 Conceptualisation of Implementation Method -- 2.1 Overall System Design. 2.2 Dataset Preparation -- 2.3 Detection Model Design -- 2.4 Web Application Deployment Design -- 3 Experiment -- 3.1 Data Pre-processing -- 3.2 Model Implementation -- 4 Analysis and Discussion -- 4.1 Model Comparison -- 4.2 Results Demonstration -- 5 Conclusion and Future Work -- References -- Coverless Video Steganography Based on Inter Frame Combination -- 1 Introduction -- 2 The Proposed Method -- 2.1 Generating Hash Sequence -- 2.2 Mapping Rule -- 2.3 Information Hiding -- 2.4 Information Extraction -- 3 Experiment Results and Analysis -- 3.1 Capacity -- 3.2 Robustness -- 3.3 Security Analysis -- 4 Conclusion -- References -- Character Photo Selection for Mobile Platform -- 1 Introduction -- 2 Related Work -- 2.1 Feature Extraction for Person -- 2.2 Photo Selection -- 3 Method -- 3.1 Proposed Framework -- 3.2 Elimination Stage -- 3.3 Selection Stage -- 4 Experiments and Analysis -- 4.1 Dataset -- 4.2 Features Importance Ranking -- 4.3 Experimental Comparison and Analysis -- 5 Conclusion -- References -- Close Euclidean Shortest Path Crossing an Ordered 3D Skew Segment Sequence -- 1 Introduction -- 2 Preliminaries -- 3 Euclidean Shortest Path Crossing a Sequence of 3D Skew Segments -- 4 Conclusion -- References -- A Lane Line Detection Algorithm Based on Convolutional Neural Network -- 1 Introduction -- 2 Method -- 2.1 Algorithm Framework -- 2.2 The Encoder -- 2.3 The Decoder -- 3 The Implementation Process -- 3.1 Remove the Full Connection Layer -- 3.2 Increase the Cavity Convolution -- 3.3 Instance Segmentation -- 3.4 Afterprocessing -- 4 Experimental Results and Analysis -- 4.1 Lane Line Detection Results -- 5 Conclusion -- References -- Segment- and Arc-Based Vectorizations by Multi-scale/Irregular Tangential Covering -- 1 Introduction -- 2 Reconstructions into Maximal Primitives -- 2.1 Multi-scale Noise Detection. 2.2 Irregular Isothetic Cyclic Representation -- 2.3 Recognition of Line Segments and Circular Arcs -- 3 Adapted Tangential Covering -- 3.1 The minDSS Algorithm -- 3.2 Adaptation of minDSS -- 4 Experimental Results -- 4.1 Global Overview of the Method -- 4.2 Visual Inspection of Results with Synthetic Images -- 4.3 Visual Inspection of Results with a Real Image -- 5 Conclusion and Future Works -- References -- Algorithms for Computing Topological Invariants in Digital Spaces -- 1 Introduction -- 2 Background Concepts of Digital Spaces -- 3 Hole Counting Algorithms in 2D -- 3.1 The Simple Formula for the Number of Holes in S -- 3.2 Algorithms for Hole Counting -- 4 Algorithms and Implementations for the Genus of Digital Surfaces in 3D -- 4.1 Practical Algorithms and Implementations -- 4.2 Implementations and Data Samples -- 5 Remarks on Programming -- 6 Summary and Discussion -- References -- Discrete Linear Geometry on Non-square Grid -- 1 Introduction -- 2 Hexagonal Grid System on a Plane -- 3 Reconstruction of Euclidean Line -- 4 Polygonalisation from Hexels -- 5 Numerical Examples -- 6 Conclusions -- References -- Electric Scooter and Its Rider Detection Framework Based on Deep Learning for Supporting Scooter-Related Injury Emergency Services -- 1 Introduction -- 1.1 Motivation -- 1.2 Proposed Idea -- 2 Background -- 2.1 Traditional Object Detection Algorithms -- 2.2 Deep Learning-Based Object Detection Algorithms -- 3 Design and Implementation -- 3.1 E-Scooter and Its Rider Detection Framework -- 3.2 E-Scooter and Its Rider Detection Model Training and Deploying -- 3.3 Fall Detection Implementation -- 4 Results and Evaluations -- 4.1 Training Model Process -- 4.2 Evaluation Model Process -- 5 Conclusion and Future Work -- References -- Tracking Livestock Using a Fully Connected Network and Kalman Filter -- 1 Introduction -- 2 Related Work. 3 Methodology -- 3.1 Object Tracker -- 3.2 Data Association -- 3.3 New and Old Tracks -- 4 Experimental Evaluation -- 4.1 Parameter Selection -- 4.2 Metrics -- 4.3 Results -- 5 Conclusion -- References -- A Comparison of Approaches for Synchronizing Events in Video Streams Using Audio -- 1 Introduction -- 2 Background -- 2.1 The Mel Spectrogram -- 2.2 Template Matching with Cross-Correlation -- 3 Data Collection -- 3.1 Database of Videos -- 3.2 Manual Annotation for Training -- 4 Methods -- 4.1 Deep Learning Model -- 4.2 Template Matching Model -- 4.3 Re-creation of Trial and Evaluation Metric -- 5 Results and Discussions -- 5.1 Deep Learning Outcome -- 5.2 Template Matching Outcome -- 5.3 Reconstruction of Trial Times from Detected Events -- 6 Conclusion -- References -- Union-Retire: A New Paradigm for Single-Pass Connected Component Analysis -- 1 Introduction -- 1.1 Union-Find Algorithms -- 1.2 Contributions -- 2 Prior Work -- 3 Union-Retire Algorithm -- 3.1 Data Structures -- 3.2 Algorithm Description -- 3.3 Example -- 4 Analysis -- 4.1 Correctness -- 4.2 Validation -- 4.3 Memory Requirements -- 4.4 Computational Complexity -- 5 Summary and Conclusion -- References -- Improving Object Detection in Real-World Traffic Scenes -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Precision and Recall -- 3.2 Score System -- 3.3 SSD Based Models -- 4 Results and Discussion -- 4.1 Model 1: Suitable CT Identification Using Default SSD -- 4.2 Model 2: Suitable Resolution Using Default SSD and CT = 0.3 -- 4.3 Model 3: Preprocessing Comparison on SSD Models with CT = 0.3 and Resolution = [800 × 600] Pixels -- 5 Conclusions -- References -- Comparison of Red versus Blue Laser Light for Accurate 3D Measurement of Highly Specular Surfaces in Ambient Lighting Conditions -- 1 Introduction -- 2 Commercial Solutions -- 3 Methodology. 4 Experiments and Results. |
| Record Nr. | UNISA-996464395003316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Geometry and vision : first International Symposium, ISGV 2021, Auckland, New Zealand, January 28-29, 2021, Revised Selected Papers / / edited by Minh Nguyen, Wei Qi Yan, and Harvey Ho
| Geometry and vision : first International Symposium, ISGV 2021, Auckland, New Zealand, January 28-29, 2021, Revised Selected Papers / / edited by Minh Nguyen, Wei Qi Yan, and Harvey Ho |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (407 pages) : illustrations |
| Disciplina | 006.37 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico | Computer vision |
| ISBN | 3-030-72073-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- A New Noise Generating Method Based on Gaussian Sampling for Privacy Preservation -- 1 Introduction -- 2 Related Work -- 2.1 Gaussian Noise Generating -- 2.2 Whittle's Noise Estimator -- 2.3 The Method Based on Fourier Transform -- 2.4 Distributed SGD for Differential Privacy -- 3 Our Methods -- 3.1 Contribution -- 3.2 The Process of Our Methods -- 3.3 Noise Variant in Stochastic Gradient Descent -- 3.4 Gaussian Distribution for Subsampling -- 4 Evaluations -- 4.1 Our Experiments for Comparing Learning Rates -- 4.2 Experiments for Gradient Clipping and Noise Levels -- 5 Conclusions -- References -- Traffic-Sign Recognition Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Research Design for Training Faster R-CNN -- 3.3 Research Design for Training Faster YOLOv5 -- 4 Results -- 4.1 Experiment Results of Faster R-CNN -- 4.2 Experiment Results of YOLOv5 -- 5 Analysis -- 6 Conclusion and Future Work -- References -- Tree Leaves Detection Based on Deep Learning -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Contribution -- 2 Literature Review -- 3 Methodology -- 3.1 Working Principle and Structure Analysis of YOLO -- 3.2 Analysis of the Working Principle of Faster R-CNN -- 3.3 Environmental Deployment -- 3.4 Data Set Preparation -- 3.5 Evaluation Methods -- 4 Analysis and Discussions -- 4.1 Comparison of Object Detection Results -- 4.2 Comparative Analysis of the Two Proposed Models -- 4.3 Discussions -- 5 Conclusion and Future Work -- References -- Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture -- 1 Introduction -- 2 Related Study -- 3 Materials and Methods -- 3.1 Encoder-Decoder Framework -- 3.2 Network Architecture Details.
4 Simulations and Results Discussion -- 4.1 Dataset Preparation -- 4.2 Quality Metrics -- 4.3 Comparison with Other State-of-the-Art Methods -- 5 Discussion and Conclusion -- References -- Apple Ripeness Identification Using Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Our Approaches -- 4 Our Experiments -- 5 Conclusion -- References -- A Hand-Held Sensor System for Exploration and Thermal Mapping of Volcanic Fumarole Fields -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 3.1 Sensor System and Registration -- 3.2 Datasets -- 3.3 Thermal Sensing Quality -- 4 Methods -- 4.1 Localization -- 4.2 3D Reconstruction - Direct -- 4.3 3D Reconstruction - Indirect -- 5 Results -- 6 Discussion -- 7 Conclusion -- References -- Traffic Sign Recognition Using Guided Image Filtering -- 1 Introduction -- 2 Literature Review -- 3 Network Design -- 3.1 Guided Image Filtering -- 3.2 Improved Faster R-CNN -- 3.3 Improved YOLOv5 -- 4 Results -- 4.1 Improved Faster R-CNN -- 4.2 Improved YOLOv5 -- 4.3 Comparsions of YOLOv5 and Faster R-CNN -- 4.4 Guided Image Filtering -- 5 Conclusion -- References -- Towards a Generic Bicubic Hermite Mesh Template for Cow Udders -- 1 Introduction -- 2 Methods -- 2.1 Data Cloud of Cow Udders -- 2.2 Bicubic Hermite Mesh -- 2.3 Coherent Point Drifting -- 3 Results -- 3.1 Morphing the CH Mesh of the Udder -- 3.2 Geometric Modelling for the Teat -- 4 Discussion -- 5 Conclusion -- References -- Sign Language Recognition from Digital Videos Using Deep Learning Methods -- 1 Introduction -- 2 Related Work -- 3 Our Method -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- New Zealand Shellfish Detection, Recognition and Counting: A Deep Learning Approach on Mobile Devices -- 1 Introduction and Backgrounds -- 2 Conceptualisation of Implementation Method -- 2.1 Overall System Design. 2.2 Dataset Preparation -- 2.3 Detection Model Design -- 2.4 Web Application Deployment Design -- 3 Experiment -- 3.1 Data Pre-processing -- 3.2 Model Implementation -- 4 Analysis and Discussion -- 4.1 Model Comparison -- 4.2 Results Demonstration -- 5 Conclusion and Future Work -- References -- Coverless Video Steganography Based on Inter Frame Combination -- 1 Introduction -- 2 The Proposed Method -- 2.1 Generating Hash Sequence -- 2.2 Mapping Rule -- 2.3 Information Hiding -- 2.4 Information Extraction -- 3 Experiment Results and Analysis -- 3.1 Capacity -- 3.2 Robustness -- 3.3 Security Analysis -- 4 Conclusion -- References -- Character Photo Selection for Mobile Platform -- 1 Introduction -- 2 Related Work -- 2.1 Feature Extraction for Person -- 2.2 Photo Selection -- 3 Method -- 3.1 Proposed Framework -- 3.2 Elimination Stage -- 3.3 Selection Stage -- 4 Experiments and Analysis -- 4.1 Dataset -- 4.2 Features Importance Ranking -- 4.3 Experimental Comparison and Analysis -- 5 Conclusion -- References -- Close Euclidean Shortest Path Crossing an Ordered 3D Skew Segment Sequence -- 1 Introduction -- 2 Preliminaries -- 3 Euclidean Shortest Path Crossing a Sequence of 3D Skew Segments -- 4 Conclusion -- References -- A Lane Line Detection Algorithm Based on Convolutional Neural Network -- 1 Introduction -- 2 Method -- 2.1 Algorithm Framework -- 2.2 The Encoder -- 2.3 The Decoder -- 3 The Implementation Process -- 3.1 Remove the Full Connection Layer -- 3.2 Increase the Cavity Convolution -- 3.3 Instance Segmentation -- 3.4 Afterprocessing -- 4 Experimental Results and Analysis -- 4.1 Lane Line Detection Results -- 5 Conclusion -- References -- Segment- and Arc-Based Vectorizations by Multi-scale/Irregular Tangential Covering -- 1 Introduction -- 2 Reconstructions into Maximal Primitives -- 2.1 Multi-scale Noise Detection. 2.2 Irregular Isothetic Cyclic Representation -- 2.3 Recognition of Line Segments and Circular Arcs -- 3 Adapted Tangential Covering -- 3.1 The minDSS Algorithm -- 3.2 Adaptation of minDSS -- 4 Experimental Results -- 4.1 Global Overview of the Method -- 4.2 Visual Inspection of Results with Synthetic Images -- 4.3 Visual Inspection of Results with a Real Image -- 5 Conclusion and Future Works -- References -- Algorithms for Computing Topological Invariants in Digital Spaces -- 1 Introduction -- 2 Background Concepts of Digital Spaces -- 3 Hole Counting Algorithms in 2D -- 3.1 The Simple Formula for the Number of Holes in S -- 3.2 Algorithms for Hole Counting -- 4 Algorithms and Implementations for the Genus of Digital Surfaces in 3D -- 4.1 Practical Algorithms and Implementations -- 4.2 Implementations and Data Samples -- 5 Remarks on Programming -- 6 Summary and Discussion -- References -- Discrete Linear Geometry on Non-square Grid -- 1 Introduction -- 2 Hexagonal Grid System on a Plane -- 3 Reconstruction of Euclidean Line -- 4 Polygonalisation from Hexels -- 5 Numerical Examples -- 6 Conclusions -- References -- Electric Scooter and Its Rider Detection Framework Based on Deep Learning for Supporting Scooter-Related Injury Emergency Services -- 1 Introduction -- 1.1 Motivation -- 1.2 Proposed Idea -- 2 Background -- 2.1 Traditional Object Detection Algorithms -- 2.2 Deep Learning-Based Object Detection Algorithms -- 3 Design and Implementation -- 3.1 E-Scooter and Its Rider Detection Framework -- 3.2 E-Scooter and Its Rider Detection Model Training and Deploying -- 3.3 Fall Detection Implementation -- 4 Results and Evaluations -- 4.1 Training Model Process -- 4.2 Evaluation Model Process -- 5 Conclusion and Future Work -- References -- Tracking Livestock Using a Fully Connected Network and Kalman Filter -- 1 Introduction -- 2 Related Work. 3 Methodology -- 3.1 Object Tracker -- 3.2 Data Association -- 3.3 New and Old Tracks -- 4 Experimental Evaluation -- 4.1 Parameter Selection -- 4.2 Metrics -- 4.3 Results -- 5 Conclusion -- References -- A Comparison of Approaches for Synchronizing Events in Video Streams Using Audio -- 1 Introduction -- 2 Background -- 2.1 The Mel Spectrogram -- 2.2 Template Matching with Cross-Correlation -- 3 Data Collection -- 3.1 Database of Videos -- 3.2 Manual Annotation for Training -- 4 Methods -- 4.1 Deep Learning Model -- 4.2 Template Matching Model -- 4.3 Re-creation of Trial and Evaluation Metric -- 5 Results and Discussions -- 5.1 Deep Learning Outcome -- 5.2 Template Matching Outcome -- 5.3 Reconstruction of Trial Times from Detected Events -- 6 Conclusion -- References -- Union-Retire: A New Paradigm for Single-Pass Connected Component Analysis -- 1 Introduction -- 1.1 Union-Find Algorithms -- 1.2 Contributions -- 2 Prior Work -- 3 Union-Retire Algorithm -- 3.1 Data Structures -- 3.2 Algorithm Description -- 3.3 Example -- 4 Analysis -- 4.1 Correctness -- 4.2 Validation -- 4.3 Memory Requirements -- 4.4 Computational Complexity -- 5 Summary and Conclusion -- References -- Improving Object Detection in Real-World Traffic Scenes -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Precision and Recall -- 3.2 Score System -- 3.3 SSD Based Models -- 4 Results and Discussion -- 4.1 Model 1: Suitable CT Identification Using Default SSD -- 4.2 Model 2: Suitable Resolution Using Default SSD and CT = 0.3 -- 4.3 Model 3: Preprocessing Comparison on SSD Models with CT = 0.3 and Resolution = [800 × 600] Pixels -- 5 Conclusions -- References -- Comparison of Red versus Blue Laser Light for Accurate 3D Measurement of Highly Specular Surfaces in Ambient Lighting Conditions -- 1 Introduction -- 2 Commercial Solutions -- 3 Methodology. 4 Experiments and Results. |
| Record Nr. | UNINA-9910484794803321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Image and vision computing : 37th international conference, IVCNZ 2022, Auckland, New Zealand, November 24-25, 2022, revised selected papers / / Wei Qi Yan, Minh Nguyen, Martin Stommel (editors)
| Image and vision computing : 37th international conference, IVCNZ 2022, Auckland, New Zealand, November 24-25, 2022, revised selected papers / / Wei Qi Yan, Minh Nguyen, Martin Stommel (editors) |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (537 pages) |
| Disciplina | 006.37 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Computer vision
Image processing - Digital techniques |
| ISBN | 3-031-25825-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Convolutional neural networks -- Generative adversarial networks -- Vision transformers -- U-Nets -- YOLO -- Imaging sensors -- Segmentation -- Visual odometry -- Image reconstruction. |
| Record Nr. | UNISA-996511871903316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Image and vision computing : 37th international conference, IVCNZ 2022, Auckland, New Zealand, November 24-25, 2022, revised selected papers / / Wei Qi Yan, Minh Nguyen, Martin Stommel (editors)
| Image and vision computing : 37th international conference, IVCNZ 2022, Auckland, New Zealand, November 24-25, 2022, revised selected papers / / Wei Qi Yan, Minh Nguyen, Martin Stommel (editors) |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (537 pages) |
| Disciplina | 006.37 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Computer vision
Image processing - Digital techniques |
| ISBN | 3-031-25825-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Convolutional neural networks -- Generative adversarial networks -- Vision transformers -- U-Nets -- YOLO -- Imaging sensors -- Segmentation -- Visual odometry -- Image reconstruction. |
| Record Nr. | UNINA-9910647773703321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition [[electronic resource] ] : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
| Pattern Recognition [[electronic resource] ] : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXV, 767 p. 352 illus., 292 illus. in color.) |
| Disciplina | 006.4 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Pattern recognition
Optical data processing Machine learning Computers Application software Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Computing Milieux Information Systems Applications (incl. Internet) |
| ISBN | 3-030-41299-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996418208103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Pattern Recognition [[electronic resource] ] : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part I / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
| Pattern Recognition [[electronic resource] ] : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part I / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXV, 931 p. 427 illus., 360 illus. in color.) |
| Disciplina | 006.4 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Pattern recognition
Computers Optical data processing Application software Computer organization Education—Data processing Pattern Recognition Information Systems and Communication Service Computer Imaging, Vision, Pattern Recognition and Graphics Computer Applications Computer Systems Organization and Communication Networks Computers and Education |
| ISBN | 3-030-41404-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Classification, Action and Video and Motion -- Object Detection and Anomaly Detection -- Segmentation, Grouping and Shape -- Face and Body and Biometrics -- Adversarial Learning and Networks -- Computational Photography -- Learning Theory and Optimization -- Applications, Medical and Robotics -- Computer Vision and Robot Vision. |
| Record Nr. | UNISA-996418207503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Pattern Recognition [[electronic resource] ] : ACPR 2019 Workshops, Auckland, New Zealand, November 26, 2019, Proceedings / / edited by Michael Cree, Fay Huang, Junsong Yuan, Wei Qi Yan
| Pattern Recognition [[electronic resource] ] : ACPR 2019 Workshops, Auckland, New Zealand, November 26, 2019, Proceedings / / edited by Michael Cree, Fay Huang, Junsong Yuan, Wei Qi Yan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XIII, 278 p. 133 illus., 109 illus. in color.) |
| Disciplina | 006.4 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Pattern recognition
Computers Computer organization Machine learning Optical data processing Application software Pattern Recognition Information Systems and Communication Service Computer Systems Organization and Communication Networks Machine Learning Image Processing and Computer Vision Computer Applications |
| ISBN | 981-15-3651-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Computer Vision for Modern Vehicles -- Advances and Applications on Generative Deep Learning Models -- Image and Pattern Analysis for Multidisciplinary Computational Anatomy -- Multi-Sensor for Action and Gesture Recognition -- Towards an Automatic Data Processing Chain for Airborne and Spaceborne Sensors. . |
| Record Nr. | UNISA-996465350203316 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Pattern Recognition : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
| Pattern Recognition : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part II / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXV, 767 p. 352 illus., 292 illus. in color.) |
| Disciplina | 006.4 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Pattern recognition
Optical data processing Machine learning Computers Application software Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Computing Milieux Information Systems Applications (incl. Internet) |
| ISBN | 3-030-41299-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910380748303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part I / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
| Pattern Recognition : 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26–29, 2019, Revised Selected Papers, Part I / / edited by Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXV, 931 p. 427 illus., 360 illus. in color.) |
| Disciplina | 006.4 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Pattern recognition systems
Computer networks Image processing - Digital techniques Computer vision Application software Computer engineering Education - Data processing Automated Pattern Recognition Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer and Information Systems Applications Computer Engineering and Networks Computers and Education |
| ISBN | 3-030-41404-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Classification, Action and Video and Motion -- Object Detection and Anomaly Detection -- Segmentation, Grouping and Shape -- Face and Body and Biometrics -- Adversarial Learning and Networks -- Computational Photography -- Learning Theory and Optimization -- Applications, Medical and Robotics -- Computer Vision and Robot Vision. |
| Record Nr. | UNINA-9910380748203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Pattern Recognition : ACPR 2019 Workshops, Auckland, New Zealand, November 26, 2019, Proceedings / / edited by Michael Cree, Fay Huang, Junsong Yuan, Wei Qi Yan
| Pattern Recognition : ACPR 2019 Workshops, Auckland, New Zealand, November 26, 2019, Proceedings / / edited by Michael Cree, Fay Huang, Junsong Yuan, Wei Qi Yan |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XIII, 278 p. 133 illus., 109 illus. in color.) |
| Disciplina | 006.4 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Pattern recognition
Computers Computer organization Machine learning Optical data processing Application software Pattern Recognition Information Systems and Communication Service Computer Systems Organization and Communication Networks Machine Learning Image Processing and Computer Vision Computer Applications |
| ISBN | 981-15-3651-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Computer Vision for Modern Vehicles -- Advances and Applications on Generative Deep Learning Models -- Image and Pattern Analysis for Multidisciplinary Computational Anatomy -- Multi-Sensor for Action and Gesture Recognition -- Towards an Automatic Data Processing Chain for Airborne and Spaceborne Sensors. . |
| Record Nr. | UNINA-9910410035303321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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